Το work with title A generalized-space expansion of support vector machines for diagnostic systems by Dimou Ioannis, Zervakis Michail is licensed under Creative Commons Attribution 4.0 International
Bibliographic Citation
I. N. Dimou and M. E. Zervakis, "A generalized-space expansion of support vector machines for diagnostic systems," in 10th IEEE International Conference on Information Technology and Applications in Biomedicine, 2010, pp. 1-5. doi: 10.1109/ITAB.2010.5687779
https://doi.org/10.1109/ITAB.2010.5687779
Support Vector Machines (SVMs) are by now an established tool used in state of the art applications in the biomedical domain. Their prevalence has unveiled both a very effective generalization capability and the inherent positive definiteness constraints in kernel selection. In this work we apply a series of composite kernel extensions stemming from nonlinear second-level kernels to standard diagnostic problems. Our aim is twofold. Firstly, to create a formulation that can accept arbitrary non-positive definite feature kernels and secondly, to allow for nonlinear second-level kernels as part of this scheme.